A practical illustration of a convolutional layer is a Waldo detector, which scans across an image to locate the character Waldo. The layer extracts small, localized windows from the input image and weights the pixel intensities using a specifically trained convolution filter, denoted as $\mathsf{V}$. The objective is to learn a filter such that locations in the image with the highest 'waldoness'—meaning they closely match the target pattern—result in peak activation values in the corresponding spatial locations of the hidden representation.